Patients with Type 1 diabetes (T1D) must receive insulin from external sources in order to regulate their blood glucose concentration (BGC). Excess glucose in the blood can damage the blood vessels. This leads to several complications such as cardiovascular disease, damage to kidneys, nerves, and eyes, and difficulty in wound healing. Diabetes is the fastest growing long-term disease that affects millions of people worldwide. Diabetes affects 25.8 million people in United States and about 5% of diabetes cases are T1D in adults. Diabetes is considered to be the seventh leading cause of death in the United States and the cost of diabetes to the nation has been estimated to be $174 billion in 2007.
Most patients with T1D either administer multiple (3-5) daily insulin injections (MDI) or use insulin pumps for continuous subcutaneous insulin infusion (CSII). In CSII, basal insulin is infused continuously and bolus insulin is administered before meals to maintain BGC in the target range (70-180 mg/dl). T1D patients may experience hypoglycemia (BGC≤70 mg/dl) episodes that may be caused by insulin doses that are too large in relation to the BGC, reduced food intake, extensive physical activity, or slow absorption of currently available “fast acting” insulins. Hypoglycemia causes dizziness, unconsciousness, and seizures and if untreated diabetic coma or death. Fear of hypoglycemia is prevalent among patients with T1D and a concern in use of insulin pumps.
An artificial pancreas (AP) will automate insulin pumps by using a controller that receives information from sensors, computes the optimal insulin amount to be infused and manipulates the infusion rate of the pump. Fear of hypoglycemia is a major concern when the insulin infusion rate is under automatic control. An AP that can predict BGC accurately and compute infusion rates that will keep BGC in the target range without causing hypoglycemia would be very beneficial. Control systems that minimize the information to be entered by the user to the AP such as meal and exercise information would make life easier for patients with T1D.
Various control strategies such as proportional-integral derivative (PID) control, model predictive control (MPC), fuzzy logic control, and generalised predictive control (GPC) have been proposed for the regulation of BGC in patients with T1D. Furthermore, a bihormonal system (insulin+glucagon) that uses PID, MPC, and GPC has also been investigated. Complexity of glucose homeostasis and the current level of technology prevents tight BGC regulation. BGC dynamics are subject specific and time varying. Since a single good model that describes BGC dynamics of individuals does not exist, models have to be developed based on measurements from individuals. A single model based on some training data is not sufficient for controller design since BGC dynamics are time varying for each individual. Also a model based on data from one person would not be suitable for representing the BGC dynamics of another person. Recursive time-series models were shown to be a powerful tool for describing and predicting the dynamics of BGC, hypoglycemia alarm systems, and closed-loop control. There is a continuing need for improved recursive model development, guaranteed stability of every model developed, and adaptive control based on these recursive models.
Meals, physical activity, and emotional state are some of the factors that affect BGC. Using information from such factors improves the performance of BGC regulation. Meal information is used by many researchers to compute the amount of insulin bolus to be infused. However, use of information manually entered by patients should be balanced with convenience and adherence. Patients may forget to enter meal information in a timely manner or make erroneous estimates about the carbohydrate content of the meal. The protein, fat, and carbohydrate ratios of the foods impact the glycemic value of the meal ingested. Thus there is a continuing need for improved method and systems for an artificial pancreas.